Predicting the Curie temperature of ferromagnets using machine learning

Abstract

The magnetic properties of a material are determined by a subtle balance between the various interactions at play, a fact that makes the design of new magnets a daunting task. High-throughput electronic structure theory may help to explore the vast chemical space available and offers a design tool to the experimental synthesis. This method efficiently predicts the elementary magnetic properties of a compound and its thermodynamical stability, but it is blind to information concerning the magnetic critical temperature. Here we introduce a range of machine-learning models to predict the Curie temperature TC of ferromagnets. The models are constructed by using experimental data for about 2500 known magnets and consider the chemical composition of a compound as the only feature determining TC. Thus we are able to establish a one-to-one relation between the chemical composition and the critical temperature. We show that the best model can predict TC's with an accuracy of about 50 K. Most importantly our model is able to make predictions in regions of the chemical space, where only a small fraction of the data was considered for training. Furthermore, it is able to assess the uncertainty of such predictions. This is demonstrated by tracing the TC of binary intermetallic alloys along their composition space and for the Al-Co-Fe ternary system.